
"""simple docstring"""



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a       :						Tuple       		=		logging.getLogger(__name__)




@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					Optional[float]			     =					field(
        default=0.0  ,    metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Whether to SortishSamler or not.'}						)
    a   :					bool			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}						)
    a   :					bool			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'whether to use adafactor'}						)
    a   :					Optional[float]			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}						)
    a   :					Optional[float]			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}						)
    a   :					Optional[float]			     =					field(default=_UpperCAmelCase  ,    metadata={'help': 'Dropout probability. Goes into model.config.'}						)
    a   :					Optional[float]			     =					field(
        default=_UpperCAmelCase  ,    metadata={'help': 'Attention dropout probability. Goes into model.config.'}						)
    a   :					Optional[str]			     =					field(
        default='linear'  ,    metadata={'help': F"""Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()						)}"""}  ,    )

"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)